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Plot an ea_data object with multiple styles

Source: R/plot-ea_data.R
plot-ea_data.Rd

Creates a time series plot for an ea_data object. Several plotting styles are available, allowing for flexible visualization of time series data, uncertainty, and anomalies.

Usage

# S4 method for class 'ea_data,missing'
plot(
  x,
  style = c("default", "ribbon", "plain", "biomass", "anomaly", "histogram", "indicator",
    "indicator_ref", "diversity", "temperature_regime", "nao_enhanced"),
  reference_period = c(1991, 2020),
  sd_threshold = 1,
  highlight_recent = TRUE,
  show_trend = TRUE,
  regime_threshold = 0.5,
  mean_line_color = "red",
  trend_line_color = "blue",
  warm_color = "red",
  cold_color = "blue",
  ...
)

Arguments

x

An ea_data object.

style

Character; One of: "default", "ribbon", "plain", "biomass", "anomaly", "histogram", "indicator", "indicator_ref", "diversity", "temperature_regime", "nao_enhanced".

reference_period

Numeric vector of length 2. Years defining reference period for standardized anomalies. Default is c(1991, 2020) for climate consistency. Used with "indicator_ref" and "temperature_regime" styles.

sd_threshold

Numeric. Number of standard deviations for threshold lines and point classification in indicator styles. Default is 1 (+/-1 SD). Use 0.5 for tighter bounds or larger values for wider bounds.

highlight_recent

Logical. Whether to highlight the most recent 5 years. Default is TRUE for indicator styles.

show_trend

Logical. Whether to add trend line and statistics. Default is TRUE for indicator styles.

regime_threshold

Numeric. Threshold for regime change detection in standardized units. Default is 0.5 standard deviations.

mean_line_color

Color for mean reference line. Default is "red".

trend_line_color

Color for trend line. Default is "blue".

warm_color

Color for warm/positive anomalies. Default is "red".

cold_color

Color for cold/negative anomalies. Default is "blue".

...

Additional arguments passed to the underlying geoms (geom_line, geom_point, geom_ribbon, geom_col, geom_errorbar).

Value

A ggplot object.

Details

The available styles are:

  • "default": A simple line plot with points.

  • "ribbon": A line plot with points and a shaded confidence interval ribbon (requires lower and upper columns in the data).

  • "plain": A line plot without points or any other embellishments.

  • "biomass": A style that mimics pacea biomass plots, featuring a bold line, points, and an optional uncertainty ribbon.

  • "anomaly": A bar plot where positive values are colored red and negative values are blue, suitable for anomaly time series.

  • "histogram": A simple bar plot showing values by year. Creates a single-layer plot that can be easily customized with additional geoms like trend lines or reference lines.

  • "indicator": Ecosystem indicator style with mean reference line and trend analysis. Shows long-term mean and recent 5-year period highlighting.

  • "indicator_ref": Indicator style with 1991-2020 climate reference period. Shows standardized anomalies relative to climate normal period.

  • "diversity": Specialized style for diversity indices with regime change detection and period comparisons.

  • "temperature_regime": Temperature anomaly visualization with regime shift detection, warm/cold period highlighting, and trend analysis.

  • "nao_enhanced": Enhanced NAO visualization with phase indicators, regime periods, and standardized anomaly coloring.

Examples

# Create sample data with uncertainty
df <- data.frame(
  year = 2000:2010,
  biomass_t = rlnorm(11, meanlog = 5, sdlog = 0.3)
)
df$lower <- df$biomass_t * 0.8
df$upper <- df$biomass_t * 1.2

# Create an ea_data object
biomass_obj <- ea_data(df,
  value_col = "biomass_t",
  data_type = "Haddock Biomass",
  region = "Georges Bank",
  location_descriptor = "5Z",
  units = "tonnes"
)

# Use different plotting styles
plot(biomass_obj, style = "default")

plot(biomass_obj, style = "ribbon")

plot(biomass_obj, style = "biomass")

plot(biomass_obj, style = "histogram")


# Histogram with custom additions
plot(biomass_obj, style = "histogram") +
  ggplot2::geom_smooth(method = "lm", se = FALSE, color = "red")
#> `geom_smooth()` using formula = 'y ~ x'